Data associated with the publication: Genes involved in carbon, nitrogen, and sulfur cycling in an important estuarine ecosystem show coherent shifts in response to changes in environmental conditions

This dataset is associated with two publications: "Spatiotemporal shifts in the distribution of genes in Chesapeake Bay: Part I- Major trends, gene-gene relationships, and environmental correlates" and "Spatiotemporal shifts in the distribution of genes in Chesapeake Bay: Part II- Relationship between genes and associated modeled rates."

Metagenomics can provide insight into microbial community metabolic potential, but understanding factors that influence gene abundance could maximize the information gained from this analysis. Gene abundances are influenced by chemical or physical conditions along with other factors, such as copy number variation between taxa or methodological issues associated with identification and classification. Here, we identify major drivers of spatiotemporal shifts in microbial gene relative abundance from multiple months, sites, and depths within Chesapeake Bay in 2017 using shotgun metagenomics. We compared changes in relative abundance of bacterial photosynthesis and nitrogen and sulfur metabolism genes with other genes and measured environmental variables. Major drivers of differences in key metabolic gene abundances are associated with environmental variables that largely change with depth and season (e.g. temperature, oxygen, phosphate). For sulfur oxidation, bacterial photosynthesis, and denitrification, genes within each process are generally significantly correlated with each other and with several environmental variables. For other processes, such as nitrification, nitrogen fixation, and dissimilatory nitrate reduction to ammonium, genes that encode enzymes within the same pathway are not well correlated. The lack of correlation typically results from differences in identified taxa carrying these genes, suggesting methodological errors or discrepancies in gene copy number between taxonomic groups. Genes or pathways strongly correlated with environmental variables and specific to and inclusive of all taxa mediating the associated process may be the most suitable as indicators of biogeochemical processes, and we compare gene abundances to model predictions in our companion paper.